This paper aims to investigate different deep neural network (DNN) approaches regarding their applicability to the audio effect removal problem, particularly by reviewing the special case of distortion removal. Firstly, we provide a comparative study of four different neural network architectures on the task of distortion removal in guitar signals when the clean signal is superimposed. Then, we compare the same architectures and one state-of-the-art declipping algorithm on the task of hard-clipping removal in guitar signals as well as generic music signals. For the evaluation, we review different metrics in their capabilities to assess the quality of the output of an audio effect removal system. Finally, we show that the Demucs architecture [1]} surpasses one state-of-the-art method in the declipping task on a large-scale music dataset. Audio examples are available online at https://joimort.github.io/distortionremoval/.
